Fragmented - AI Developer Podcast

Claude Code and Codex have been the mainstay agent harnesses, with OpenCode and Pi as the popular open-source alternatives. But two new entries are pushing what a harness even is: DeepSeek's Cordis kernel, where everything — down to the agent loop itself — is a plugin you can hot-swap at runtime, and Prime Agent, an RLM-based harness that treats sub-agents and context as function calls in a live REPL. We break down what actually makes each one different.
Full shownotes: https://fragmentedpodcast.com/episodes/311

Mitchell Hashimoto co-founded HashiCorp, built some of the most impressive DevOps tools like Vagrant and Terraform, sold the company to IBM — and then built a terminal. Ghostty is now where a huge chunk of agentic coding actually happens. Mitchell was an AI skeptic. We walk through his six-step adoption framework and the workflows he uses day to day — warm-start research, Hail Mary prompts across twenty GitHub issues, and knowing when to let the agent slam dunk it.

Full shownotes at fragmentedpodcast.com.

Show Notes

HashiCorp

Vagrant

Terraform

IBM acquires Hashicorp



Ghostty

Ghostty - Mitchell's fast, native terminal built for platform integration across Mac and Linux

Terminal shell

SSH - secure shell

PTY - pseudoterminals

Terminal Multiplexers

tmux - most popular open source one



XTGETTCAP by xterm

libghostty - the cross-platform terminal emulation library that powers Ghostty's core

xterm-js - powers terminal for apps like VSCode and the cloud

Jedi Term - Intellij's embedded terminal

Ghostty is now a non-profit

cmux - native macOS terminal

multiplexer built on libghostty — a fork Mitchell champions

Free Software Definition -

the 4 essential freedoms

The freedom to run the program as you wish, for any purpose.

The freedom to study how the program works, and change it to make it do

what you wish.

The freedom to redistribute copies so you can help others.

The freedom to distribute copies of your modified versions to others.



Mitchell's tweet on unsolicited PRs and transfer of ownership

The AI Adoption Journey

My AI Adoption Journey -

Mitchell's blog post outlining his five-step framework

Step 1: Drop the Chatbot

Episode 301 - AI Coding ladder - Different stages of AI

adoption



Step 2: Reproduce Your Own Work

Step 3: End-of-Day Agents

OpenAI Deep Research -

kick off research tasks for a "warm start" the next morning

Spine AI research - deep research tool for

longer, hour-long analysis tasks



Step 4: Outsource the Slam Dunks

Claude status hooks - warcraft peons

Conductor



Step 5: Engineer the Harness

Episode 307 - Harness Engineering - Fragmented's deep dive

on harness engineering, heavily inspired by Mitchell's post



Step 6: Always have an Agent running

Peter Steinberger

Codex plugin for Claude Code

Get in touch

We'd love to hear from you. Email is the best way to reach us or you can check our contact page for other ways.

We want to hear all the feedback: what's working, what's not, topics you'd like to hear more on.

Contact us

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Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind

our new direction.

Andrej Karpathy says the goal is to maximize how long an agent runs without your intervention. But there's a false summit most teams hit first: individual speed goes up while system speed stalls, your laptop roars under four parallel Gradle builds, and review queues back up. Kaushik and Iury trace the full arc — from local multitasking to cloud-hosted async work to fully autonomous agents that fire on repo events and put PRs in your inbox.

Show Notes

Andrej Karpathy on agents and token throughput - NoPriors podcast — maximize agent runtime, not token burn

Cursor Agent Mode - Multiagent interface - introduced the multi-agent board as a new paradigm for local parallel agents

Google Antigravity - Agent Manager interface

Claude Code Agent Teams - spawn

sub-agents from a main orchestrator, with tmux pane integration

Git worktrees - /reddit

Remote Background Agents in the cloud

Google Jules - hosted GitHub-connected agent,

proposes a plan, edits code, runs tests, opens a PR

Cursor Cloud Agents - remote agents

that clone your repo in the cloud and work in parallel

OpenAI Codex - cloud software

engineering agent for parallel tasks

Claude Code on the web - cloud-hosted Claude Code

sessions decoupled from your local machine

Building trust

Episode 307 - Harness Engineering - the earlier episode on

shaping agent environments — and why this ceiling exists

Get in touch

We'd love to hear from you. Email is the best way to reach us or you can check our contact page for other ways.

We want to hear all the feedback: what's working, what's not, topics you'd like to hear more on.

Contact us

Newsletter

Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind

our new direction.

You already know how LLMs work from our popular 20-minute explainer. Now we take it to images. What does Michelangelo have to do with stable diffusion? More than you'd think. Walk away knowing how image generation actually works — and what it has in common with the text models you already understand.

Full shownotes at fragmentedpodcast.com.

Show Notes

Episode 303 - How LLMs work in 20 minutes - text generation

VAE -
Variational Autoencoder

RGB Color model - wikipedia

Word2Vec technique - wikipedia

Efficient Estimation of Word Representation -

original Word2Vec paper by Mikolov et al.



High-Resolution Image Synthesis with Latent Diffusion Models -

Rombach et al. (2022) — the paper behind Stable Diffusion

Image Training data

LAION-5B - 5 billion image-text pairs

scraped from the web, used to train many image generation models

WebLI - Google's internal image-text

dataset



Michelangelo

Get in touch

We'd love to hear from you. Email is the

best way to reach us or you can check our contact page for other

ways.

We want to hear all the feedback: what's working, what's not, topics you'd like

to hear more on.

Contact us

Newsletter

Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind

our new direction.

The hard part of AI coding isn't generating code — it's controlling quality, safety, and drift. Kaushik and Iury break down harness engineering: the five pillars for shaping an agent's environment and what it looks like when teams build custom harnesses from scratch.

Full shownotes at fragmentedpodcast.com.

Show Notes

Why it matters

Harness Engineering -

OpenAI's post on building their Codex codebase (~1M lines of code, 1,500 PRs

merged, zero manually written)

Shaping the harness

The Feed's Lost and Found -

Iury's newsletter consolidating harness engineering themes

Agent legibility

Closed feedback loops

Persistent memory

Entropy control

Blast radius controls

Building the harness

Minions: Stripe's one-shot, end-to-end coding agents -

Stripe forked Goose to build custom agents for their codebase

Goose - open-source coding agent from Block

Superpowers by Jesse Vincent - skills

that enforce a proper software engineering process

Open Code - open-source coding agent you can fork and

customize

Other resources

Agent Harness Glossary -

Latent Patterns

Towards self-driving codebases -

Cursor

Agentic Workflows -

GitHub Next

Future of Software Development -

ThoughtWorks

Get in touch

We'd love to hear from you. Email is the

best way to reach us or you can check our contact page for other

ways.

We want to hear all the feedback: what's working, what's not, topics you'd like

to hear more on.

Contact us

Newsletter

Youtube

Website

Co-hosts:

Kaushik Gopal

Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind

our new direction.